Abstract

There has been much recent speculation that global warming may allow the
reestablishment of malaria transmission in previously endemic areas such as
Europe and the United States. In this report we analyze temporal trends in
malaria in Britain between 1840 and 1910, to assess the potential for
reemergence of the disease. Our results demonstrate that at least 20% of the
drop-off in malaria was due to increasing cattle population and decreasing
acreages of marsh wetlands. Although both rainfall and average temperature
were associated with year-to-year variability in death rates, there was no
evidence for any association with the long-term malaria trend. Model
simulations for future scenarios in Britain suggest that the change in
temperature projected to occur by 2050 is likely to cause a proportional
increase in local malaria transmission of 8–14%. The current risk is
negligible, as >52,000 imported cases since 1953 have not led to any
secondary cases. The projected increase in proportional risk is clearly
insufficient to lead to the reestablishment of endemicity.

The British Chief Medical Officer's recent report asserted that “by
2050 the climate of the U[nited] K[ingdom] may be such that indigenous malaria
could become re-established”
(1). This prediction was based
on model simulations, derived from only a subset of the possible links between
climate and malaria (2,
3). To be confident in future
predictions, we first need to understand the past by quantifying the factors
that governed the disappearance of malaria from Britain. In this article, we
describe statistical modeling of the determinants of temporal trends in
malaria deaths within English counties and Wales from 1840–1910.

From historical records, we know that a malarious illness referred to as
“the ague” or “intermittent fever” caused high levels
of mortality in the British marshlands and fens from the 15th to the 19th
century (4,
5). Robust evidence that the
illness was malaria emerged in the early 19th century, when the increasing use
of quinine and advances in fever diagnosis and pathology created a distinct
separation from other acute fevers. Definitions of ague in 19th-century
medical textbooks uniquely indicate malaria as they invariably refer to
noncontagious transmission, distinctive cold, hot, and sweating stages,
tertian onset of symptoms, cycling relapses, anemia, splenomegaly or
“ague cake,” and susceptibility to quinine
(6,
7). The remarkable virulence of
this disease in England, given that the pathogen responsible was presumably
Plasmodium vivax, has never been explained satisfactorily
(5). The situation in Britain
was not unique. In Holland at the end of the 19th century, equally high death
rates were reported from intermittent fevers, believed to be caused by P.
vivax (8). Although
currently responsible for 80 million annual cases of malaria worldwide, P.
vivax is not now a lethal parasite
(9). One possible explanation
for the high malaria mortality rates observed in 19th-century Europe is the
high likelihood of coinfections with pathogens associated with poor
sanitation.

Physicians throughout Britain reported a decline in ague cases from the
early 1800s (10). Many
hypotheses have since been proposed to explain this gradual disappearance
(8). Marsh drainage could have
eliminated many breeding sites of the main local vector, Anopheles
atroparvus, in the brackish waters of coastal marshes, river deltas, and
fens (11). A.
atroparvus feeds mainly on livestock but will take human blood when
available. Hence, increasing livestock densities may have diverted biting from
humans toward cattle, pigs, or horses. Improved housing, better access to
health care and medication, and improved nutrition, sanitation, and hygiene
all may have reduced transmission and/or mortality rates. Although these
theories are widely accepted, the relative importance of the different factors
has been discussed only rarely and has never been quantified.

In this article, we analyze the role of climate, agricultural factors, and
land cover in the temporal variation in ague (malaria) death rates in Britain
during the 19th century. We selected 1840 as a starting point, because at this
time acute fever diagnosis was sufficiently accurate that the great majority
of reported ague deaths were probably due to malaria. To provide further
evidence of the accuracy of these diagnoses, we also investigate the factors
associated with spatial variability in ague death rates (expected to be
similar to the determinants of temporal variability) and with temporal
variability in other causes of death (expected to be different). The study
terminates in 1910, the last year that ague deaths were reported at county
level.

Data

Annual ague death counts from 43 counties (including North and South Wales;
see Fig. 2) over the 71-year
period were collected from historical records of the Registrar General.
Explanatory data comprised population size, annual minimum, mean, and maximum
average temperature, total precipitation, pig and cattle density per 100
acres, and percentage coverage by crops and inland water (wetlands) for each
county in each year (Table 1).
Data on water acreage per county (inland water including marshes, rivers,
lakes, and brackish water areas) were collected annually from the Report of
the Registrar General. Crop acreage per county (all crops, bare fallow, and
grasses) and the number of pigs and cattle at county level were recorded at
5-year intervals from the Ministry of Agriculture, Fisheries, and Food. Annual
population measures were obtained from the Report of the Registrar General
(interpolated from censuses undertaken every 10 years). Water and crop acreage
was converted to percentage of total county size (from the Report of the
Registrar General), and pig and cattle numbers were converted to animal
density per 100 acres (2.5 km2) of land. Annual crop acreage and
pig and cattle densities for intervening years were calculated by linear
regression between the observed time points. Climate measurements for each
county and year were based on monthly average data on minimum, maximum, and
average temperature and total precipitation, which were taken from historical
meteorological data from 104 stations (up to 1901) and from the 0.5°
gridded 1901–1995 Climatic Research Unit (CRU) climate data set
(12) for later years. Although
station data extend beyond 1901, it was decided to use the CRU gridded data
set for simplicity and better coverage. Data from the two sources did not
differ significantly; for example, in 1905, the mean average temperature
measured from London meteorological stations was 9.18°C, compared with a
calculated value of 9.14°C from the gridded data. Estimates for years up
to 1901 were derived by calculating the average of all georeferenced
meteorological stations within the county borders (as shown on an 1843 map of
Britain). Between 1901 and 1910, measurements represent the average of all
0.5° grid cells where the majority of the cell lay inside each county.
Temperatures were converted to average annual minima, maxima, and averages,
and precipitation was converted to annual totals. Explanatory and outcome data
were collected from 43 counties (including North and South Wales) over the
71-year period.

Table 1.Demographic and agricultural explanatory variables: Britain totals at
the start, middle, and end of the study period

A database listing ague deaths, all-cause mortality, and explanatory
variables for each county in each year during 1840–1910 was assembled
and used for the generation of spatial and temporal multiple logistic models
in STATA 7 (Stata, College Station, TX), by using county population sizes each
year (interpolated from 10-year census data) as denominators.

Statistical Methods

Using multiple logistic regression, we investigated the role of these
explanatory variables in both the temporal and spatial variation in ague death
rates. The temporal model was designed to investigate the interannual
variation in county ague deaths without drawing on information from
differences in average rates between counties. To do this, we included a
categorical county indicator variable (i.e., stratifying by county). National
rates show a strong decrease over time, but it is impossible to distinguish in
this single series how much of this decrease is due to explanatory variables
that show similar temporal trends (e.g., wetland coverage) or to factors not
included in the analysis. We answer these questions instead by opting for the
conservative approach of assessing how well the explanatory variables explain
temporal deviations from the national downward trend in county-specific rates.
This was done by including the national trend in ague rates in the model,
i.e., by incorporating calendar year as a continuous variable. The year
variable showed a significant nonlinear relationship with ague death rates,
which was captured by including both linear and quadratic terms. Ague rates
were found to have a significant temporal autocorrelation of order one (one
year's rate was predicted by the previous year's rate). This autocorrelation
was incorporated into the model by inclusion of the lagged residual (i.e., the
difference between observed ague deaths and those predicted by the null model
containing the categorical county indicator, the temporal trend, and the
temporal autocorrelation; ref.
13).

The spatial model was designed to investigate the intercounty variation in
ague deaths without drawing information from variation in national rates
between years, by including a calendar year categorical indicator variable
(i.e., stratifying by year). To avoid complexity, we did not control for
spatial autocorrelation (i.e., nearby counties having similar rates). It is
unlikely that this method biased estimates of effects, but it may have
underestimated the width of confidence intervals. The confidence intervals
given for deaths predicted by all our models reflect uncertainty in parameter
estimates but not in the model-building process.

For both the temporal and the spatial modeling scenario, redundant
variables were excluded by using a backward stepwise approach, achieving
minimal adequate models in which all remaining variables were significant
(P < 0.05) (14).
However, for comparability, the temporal model for all-cause mortality
included those variables identified as significant by the stepwise process for
ague death rates. For comparative purposes, we also investigated how the same
explanatory variables were related to temporal patterns in all-cause mortality
(i.e., all deaths excluding ague, accidental and violent deaths, suicide, and
stillbirths).

All models were scaled for overdispersion by using the Pearson
χ2 statistics divided by the degrees of freedom as the scale
parameter (15).

Finally, we used our model for temporal variability in ague to predict the
number of ague deaths in Britain during 1840–1910 in “virtual
experiments,” in which we measured the change in the expected number of
deaths had the climate been warmer or had one or more of the significant
nonclimatic factors remained at 1840 levels. We ran the model six times to
test the impact of six possible sets of circumstances, as described in
Table 3. These simulations were
carried out by altering the values of one or more of the four explanatory
variables for each county and each year. The predicted number of cases in each
county and each year was then estimated by using the ague death rates fitted
by the temporal ague model (incorporating the underlying national trend and
temporal autocorrelation), combined with the appropriate population size.
These numbers were then totaled for each simulation
(Table 3).

Results and Discussion

From 1840 to 1910, a total of 8,209 ague deaths was reported. These deaths
declined steadily over time, with distinct epidemics in 1848 and 1859
(Fig. 1). The highest rates
were reported from Kent, Essex, and Cambridgeshire
(Fig. 2), consistent with
historical observations of high ague mortality in coastal and marshy areas
(10,
16,
17) where the principal
mosquito vector, A. atroparvus, is still present
(18).

Observed and predicted ague deaths in Britain from 1840 to 1910 are
shown.

Temporal variability in ague death rates was significantly associated with
changes in inland water coverage, mean temperature, and total precipitation
(all positive) and cattle density (negative; see
Table 2). Omitting the least
malarious counties (i.e., analyzing only counties with malaria death rates
>60 or 30 per 100,000 inhabitants during the 70 years) had no significant
impact on the coefficient values for the four explanatory variables. Hence,
the full analysis (Table 2) did
not significantly underestimate their effects by including possibly
nonmalarious counties. The departures from the overall temporal trend,
including the two epidemics of 1848 and 1859, are well predicted by the model
(Fig. 1), although the peaks of
the predicted values are dampened because of the inclusion of the previous
year's residuals. This result suggests that the epidemics were partly due to
above average temperatures and/or precipitation
(Fig. 3).

Spatial variation in ague was also significantly associated with inland
water, cattle density, mean temperature, and total precipitation
(Table 2) with directions the
same as those of the temporal model. In contrast, the four explanatory
variables for ague death rates had opposite (inland water, cattle density, and
precipitation) or no effect (average temperature) on temporal variation in
all-cause mortality (Table 2),
indicating that the variation in ague was not merely reflecting factors which
affected general mortality.

Using the model for temporal variation in ague, we simulated malaria
incidence during 1840–1910 for various scenarios
(Table 3). If cattle densities
or inland water coverage had remained at 1840 levels, we predicted that the
number of ague deaths would have been 8.7% and 10.8% greater, respectively
(Table 3), from 1840 to 1910.
Had cattle densities and inland water coverage both remained unchanged, they
would have been 19.5% greater. These results conclusively demonstrate the
important, largely independent, and roughly equivalent roles of the changes in
cattle densities and wetland coverage in the disappearance of malaria from
Britain. Our estimates of the impact of both factors are potentially
conservative for three reasons. First, much of the variation in ague was
already removed by the year trend. When we excluded the trend from the model,
a 4-fold increase in the apparent effect of cattle density was detected, but
no significant change in the effect of inland water coverage (data not
shown).

Second, local environmental factors cannot explain variation due to
imported cases. These were not distinguished in reports until 1919. Imported
cases typically increase immediately after overseas wars in malarious zones,
as shown by the steady decrease in the number of imported cases reported from
1919 until the early 1920s. However, our study period was relatively peaceful
except for the Crimean War (1853–1856), which did not coincide with
either of the epidemics. The mean annual number of imported cases reported in
the first “stable” decade (i.e., 1926–1935) was 464, which
is still relatively high, suggesting that at least some of the malaria deaths
notified before 1911 were imported.

Third, although our models incorporate the effect of the previous year's
residuals, we did not revise these values for the simulations (which would
have generated a positive feedback, in the absence of any dampening regulatory
factor in the model). Hence, we did not account for the cumulative effect of
any consistent simulated change in risk over time. Spatial differences, unlike
one-year temporal differences, should reflect the cumulative effect of a
consistent difference in risk. However, none of the coefficient values for the
spatial ague models were significantly greater than those for the temporal
models, indicating that the cumulative effect is marginal.

Average temperatures in Britain from 1840 to 1910 varied annually across a
2°C range. Our simulations (with similar caveats) indicate that a 1°C
increase or decrease was responsible for an increase in malaria deaths of 8.3%
or a decrease of 6.5%, respectively (Table
3). This contradicts Reiter's claim
(19) that climate was
relatively unimportant, based on his observation that a drastic drop in
average annual temperatures during the 16th to the 18th centuries had no
marked effect on the mentions of ague in historical literature or total
mortality in the Kent and Essex marshes. In contrast, Lindsay and Joyce
(20) argued that a period of
successive cold summers in the 1800s (observed from records of the Central
England Temperature Series, a monthly composite of station data) coincided
with a decline in infant mortality rate (a proxy measure of ague death rates)
and may have helped push malaria toward extinction. Our interpretation, based
on analysis of concurrent climate, agricultural, and malaria data, differs
from both of the above. We show a significant effect of both temperature and
precipitation, explaining the malaria epidemics in the “unusually hot
summers” of 1848 and 1859
(10,
16,
21–23).
However, outside these epidemics, the long-term trend during the 19th century
was surprisingly consistent, with no noticeable increase in the rate of
decline in the 1860s, and was probably driven by nonclimatic factors.

The most commonly used climate change models predict a rise between 1°C
and 2.5°C in average United Kingdom temperatures by the 2050s
(24). Assuming that the
proportional impact of temperature will be the same in the 21st century as it
was in the 19th and 20th centuries, our simulations indicate that global
warming could increase the risk of local malaria transmission by 8–15%.
However, between 1940 and 1981, 20,000 km2 of wet ground was
drained in Britain; in East Anglia alone, 90% of fens have been lost since
1934 [M. Millett (Wetlands Advisory Service), personal communication]. Thus,
socioeconomic and agricultural changes have created a country in which
R0 (the number of secondary cases arising from each
primary case in a susceptible population) is currently minuscule, as
demonstrated by the absence of any secondary malaria cases in this country
since 1953 (25), despite a
cumulative reported total of 52,907 imported cases. The national health system
ensures that imported malaria infections are detected and effectively treated
and that gametocytes are cleared from the blood in less than a week (D.
Warhurst, personal communication). This trend should continue in the future,
unless malaria treatment becomes ineffective because of, for example, drug
resistance. In Britain, a 15% rise in risk might have been important in the
19th century, but such a rise is now highly unlikely to lead to the
reestablishment of indigenous malaria.

Acknowledgments

We thank Phil Rogers and David Lister for provision of climate data; Matt
Millett for information on United Kingdom wetlands; and David Bradley, Paul
Coleman, Jon Cox, Chris Curtis, Bo Drasar, Vanessa Harding, and David Warhurst
for advice on historical malaria and comments on the manuscript. D.H.C.-L. is
supported by the Wellcome Trust, and K.G.K. is supported by the Løvens
Kemiske Fabrik, Roblon Foundation, Obel Foundation, and Novo Nordisk
Foundation. K.G.K., D.H.C.-L., B.A., and C.R.D. are members of the Medical
Research Council and Natural Environment Research Councils (MRC-NERC)
Cooperative Group on Climate Change, Ozone Depletion, and Human Health formed
by the London School of Hygiene and Tropical Medicine and the University of
East Anglia.

Footnotes

↵†
To whom correspondence should be addressed. E-mail:
katrin.kuhn{at}lshtm.ac.uk.

Whitley, G. (1863) Report by Dr. George
Whitley as to the Quantity of Ague and other Malarious Diseases now Prevailing
in the Principal Marsh Districts of England, Sixth report of the
medical officer of the privy council (Dept. of Health, London).

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